Overview

Dataset statistics

Number of variables12
Number of observations401
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.1 KiB
Average record size in memory204.6 B

Variable types

Numeric8
DateTime1
Categorical3

Alerts

Index is highly overall correlated with PM10 (µg/m³) and 6 other fieldsHigh correlation
PM10 (µg/m³) is highly overall correlated with Index and 6 other fieldsHigh correlation
NO2 (ppb) is highly overall correlated with Index and 6 other fieldsHigh correlation
SO2 (ppb) is highly overall correlated with Index and 6 other fieldsHigh correlation
O3 (ppb) is highly overall correlated with Index and 6 other fieldsHigh correlation
Temperature (°C) is highly overall correlated with Index and 6 other fieldsHigh correlation
Humidity (%) is highly overall correlated with Index and 6 other fieldsHigh correlation
PM2.5 is highly overall correlated with Index and 6 other fieldsHigh correlation
Location is highly overall correlated with CO (ppm)High correlation
CO (ppm) is highly overall correlated with LocationHigh correlation
Index is uniformly distributedUniform

Reproduction

Analysis started2023-10-22 05:09:34.917901
Analysis finished2023-10-22 05:10:09.017055
Duration34.1 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Index
Real number (ℝ)

HIGH CORRELATION  UNIFORM 

Distinct400
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.95761
Minimum1
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:09.215208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1101
median201
Q3301
95-th percentile381
Maximum400
Range399
Interquartile range (IQR)200

Descriptive statistics

Standard deviation115.83273
Coefficient of variation (CV)0.5764038
Kurtosis-1.2023234
Mean200.95761
Median Absolute Deviation (MAD)100
Skewness-0.0019221089
Sum80584
Variance13417.221
MonotonicityIncreasing
2023-10-22T05:10:09.571289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
384 2
 
0.5%
1 1
 
0.2%
264 1
 
0.2%
273 1
 
0.2%
272 1
 
0.2%
271 1
 
0.2%
270 1
 
0.2%
269 1
 
0.2%
268 1
 
0.2%
267 1
 
0.2%
Other values (390) 390
97.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
400 1
0.2%
399 1
0.2%
398 1
0.2%
397 1
0.2%
396 1
0.2%
395 1
0.2%
394 1
0.2%
393 1
0.2%
392 1
0.2%
391 1
0.2%

Date
Date

Distinct101
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2023-01-01 00:00:00
Maximum2023-12-03 00:00:00
2023-10-22T05:10:09.900714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:10.192827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Location
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
Mumbai
103 
Pune
101 
Nagpur
99 
Thane
98 

Length

Max length6
Median length6
Mean length5.2518703
Min length4

Characters and Unicode

Total characters2106
Distinct characters15
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowPune
3rd rowNagpur
4th rowThane
5th rowMumbai

Common Values

ValueCountFrequency (%)
Mumbai 103
25.7%
Pune 101
25.2%
Nagpur 99
24.7%
Thane 98
24.4%

Length

2023-10-22T05:10:10.477898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T05:10:10.804380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
mumbai 103
25.7%
pune 101
25.2%
nagpur 99
24.7%
thane 98
24.4%

Most occurring characters

ValueCountFrequency (%)
u 303
14.4%
a 300
14.2%
n 199
9.4%
e 199
9.4%
M 103
 
4.9%
m 103
 
4.9%
b 103
 
4.9%
i 103
 
4.9%
P 101
 
4.8%
N 99
 
4.7%
Other values (5) 493
23.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1705
81.0%
Uppercase Letter 401
 
19.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 303
17.8%
a 300
17.6%
n 199
11.7%
e 199
11.7%
m 103
 
6.0%
b 103
 
6.0%
i 103
 
6.0%
g 99
 
5.8%
p 99
 
5.8%
r 99
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
M 103
25.7%
P 101
25.2%
N 99
24.7%
T 98
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2106
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
u 303
14.4%
a 300
14.2%
n 199
9.4%
e 199
9.4%
M 103
 
4.9%
m 103
 
4.9%
b 103
 
4.9%
i 103
 
4.9%
P 101
 
4.8%
N 99
 
4.7%
Other values (5) 493
23.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
u 303
14.4%
a 300
14.2%
n 199
9.4%
e 199
9.4%
M 103
 
4.9%
m 103
 
4.9%
b 103
 
4.9%
i 103
 
4.9%
P 101
 
4.8%
N 99
 
4.7%
Other values (5) 493
23.4%

PM10 (µg/m³)
Real number (ℝ)

HIGH CORRELATION 

Distinct349
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8858.1347
Minimum18
Maximum39456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:11.078782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q182
median2158
Q315398
95-th percentile33962
Maximum39456
Range39438
Interquartile range (IQR)15316

Descriptive statistics

Standard deviation11469.803
Coefficient of variation (CV)1.2948327
Kurtosis0.12277934
Mean8858.1347
Median Absolute Deviation (MAD)2133
Skewness1.186254
Sum3552112
Variance1.3155638 × 108
MonotonicityNot monotonic
2023-10-22T05:10:11.383918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 5
 
1.2%
26 5
 
1.2%
22 4
 
1.0%
27 4
 
1.0%
25 4
 
1.0%
23 4
 
1.0%
29 4
 
1.0%
30 4
 
1.0%
24 4
 
1.0%
32 3
 
0.7%
Other values (339) 360
89.8%
ValueCountFrequency (%)
18 1
 
0.2%
19 2
 
0.5%
20 3
0.7%
21 3
0.7%
22 4
1.0%
23 4
1.0%
24 4
1.0%
25 4
1.0%
26 5
1.2%
27 4
1.0%
ValueCountFrequency (%)
39456 1
0.2%
39172 1
0.2%
38889 1
0.2%
38607 1
0.2%
38326 1
0.2%
38046 1
0.2%
37767 1
0.2%
37489 1
0.2%
37212 1
0.2%
36936 1
0.2%

CO (ppm)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
0.2
103 
0.3
101 
0.4
99 
0.5
98 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1203
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.2
2nd row0.3
3rd row0.4
4th row0.5
5th row0.2

Common Values

ValueCountFrequency (%)
0.2 103
25.7%
0.3 101
25.2%
0.4 99
24.7%
0.5 98
24.4%

Length

2023-10-22T05:10:11.709479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T05:10:12.025236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.2 103
25.7%
0.3 101
25.2%
0.4 99
24.7%
0.5 98
24.4%

Most occurring characters

ValueCountFrequency (%)
0 401
33.3%
. 401
33.3%
2 103
 
8.6%
3 101
 
8.4%
4 99
 
8.2%
5 98
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 802
66.7%
Other Punctuation 401
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 401
50.0%
2 103
 
12.8%
3 101
 
12.6%
4 99
 
12.3%
5 98
 
12.2%
Other Punctuation
ValueCountFrequency (%)
. 401
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1203
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 401
33.3%
. 401
33.3%
2 103
 
8.6%
3 101
 
8.4%
4 99
 
8.2%
5 98
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 401
33.3%
. 401
33.3%
2 103
 
8.6%
3 101
 
8.4%
4 99
 
8.2%
5 98
 
8.1%

NO2 (ppb)
Real number (ℝ)

HIGH CORRELATION 

Distinct358
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22124.681
Minimum10
Maximum149199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:12.317225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q170
median2890
Q331647
95-th percentile105339
Maximum149199
Range149189
Interquartile range (IQR)31577

Descriptive statistics

Standard deviation34512.119
Coefficient of variation (CV)1.5598923
Kurtosis2.7751188
Mean22124.681
Median Absolute Deviation (MAD)2876
Skewness1.8603242
Sum8871997
Variance1.1910863 × 109
MonotonicityNot monotonic
2023-10-22T05:10:12.629381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 5
 
1.2%
16 5
 
1.2%
19 5
 
1.2%
10 4
 
1.0%
18 4
 
1.0%
11 4
 
1.0%
14 4
 
1.0%
17 4
 
1.0%
20 4
 
1.0%
12 4
 
1.0%
Other values (348) 358
89.3%
ValueCountFrequency (%)
10 4
1.0%
11 4
1.0%
12 4
1.0%
13 5
1.2%
14 4
1.0%
15 3
0.7%
16 5
1.2%
17 4
1.0%
18 4
1.0%
19 5
1.2%
ValueCountFrequency (%)
149199 1
0.2%
146652 1
0.2%
144147 1
0.2%
141684 1
0.2%
139262 1
0.2%
136881 1
0.2%
134540 1
0.2%
132239 1
0.2%
129978 1
0.2%
127755 1
0.2%

SO2 (ppb)
Real number (ℝ)

HIGH CORRELATION 

Distinct319
Distinct (%)79.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8854.0075
Minimum5
Maximum51955
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:12.969077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q127
median1353
Q313967
95-th percentile39161
Maximum51955
Range51950
Interquartile range (IQR)13940

Descriptive statistics

Standard deviation12944.301
Coefficient of variation (CV)1.4619709
Kurtosis1.5732254
Mean8854.0075
Median Absolute Deviation (MAD)1346
Skewness1.5828099
Sum3550457
Variance1.6755494 × 108
MonotonicityNot monotonic
2023-10-22T05:10:13.302411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 12
 
3.0%
7 11
 
2.7%
9 10
 
2.5%
6 9
 
2.2%
10 5
 
1.2%
5 4
 
1.0%
11 4
 
1.0%
24 4
 
1.0%
23 4
 
1.0%
17 4
 
1.0%
Other values (309) 334
83.3%
ValueCountFrequency (%)
5 4
 
1.0%
6 9
2.2%
7 11
2.7%
8 12
3.0%
9 10
2.5%
10 5
1.2%
11 4
 
1.0%
12 3
 
0.7%
13 2
 
0.5%
14 1
 
0.2%
ValueCountFrequency (%)
51955 1
0.2%
51157 1
0.2%
50380 1
0.2%
49624 1
0.2%
48887 1
0.2%
48168 1
0.2%
47467 1
0.2%
46784 1
0.2%
46117 1
0.2%
45466 1
0.2%

O3 (ppb)
Real number (ℝ)

HIGH CORRELATION 

Distinct362
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161469.7
Minimum33
Maximum17150813
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:13.610943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile39
Q1110
median4015
Q3248935
95-th percentile9065654
Maximum17150813
Range17150780
Interquartile range (IQR)248825

Descriptive statistics

Standard deviation3147975.2
Coefficient of variation (CV)2.7103378
Kurtosis10.52187
Mean1161469.7
Median Absolute Deviation (MAD)3977
Skewness3.3020177
Sum4.6574934 × 108
Variance9.9097475 × 1012
MonotonicityNot monotonic
2023-10-22T05:10:13.935357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 5
 
1.2%
42 5
 
1.2%
38 5
 
1.2%
41 4
 
1.0%
39 4
 
1.0%
44 4
 
1.0%
35 3
 
0.7%
36 3
 
0.7%
43 3
 
0.7%
37 3
 
0.7%
Other values (352) 362
90.3%
ValueCountFrequency (%)
33 1
 
0.2%
34 2
 
0.5%
35 3
0.7%
36 3
0.7%
37 3
0.7%
38 5
1.2%
39 4
1.0%
40 5
1.2%
41 4
1.0%
42 5
1.2%
ValueCountFrequency (%)
17150813 1
0.2%
16631234 1
0.2%
16133589 1
0.2%
15657526 1
0.2%
15202695 1
0.2%
14768747 1
0.2%
14355332 1
0.2%
13962102 1
0.2%
13588709 1
0.2%
13234806 1
0.2%

Temperature (°C)
Real number (ℝ)

HIGH CORRELATION 

Distinct370
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.73616
Minimum22
Maximum95.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:14.253619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile23.3
Q139
median55
Q374.6
95-th percentile91.2
Maximum95.2
Range73.2
Interquartile range (IQR)35.6

Descriptive statistics

Standard deviation21.295793
Coefficient of variation (CV)0.37534781
Kurtosis-1.1143764
Mean56.73616
Median Absolute Deviation (MAD)17.9
Skewness0.064563833
Sum22751.2
Variance453.51081
MonotonicityNot monotonic
2023-10-22T05:10:14.552652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 3
 
0.7%
51.2 3
 
0.7%
22.5 2
 
0.5%
53.2 2
 
0.5%
54.8 2
 
0.5%
54.6 2
 
0.5%
54.4 2
 
0.5%
54.2 2
 
0.5%
54 2
 
0.5%
53.8 2
 
0.5%
Other values (360) 379
94.5%
ValueCountFrequency (%)
22 1
 
0.2%
22.2 1
 
0.2%
22.3 2
0.5%
22.4 1
 
0.2%
22.5 2
0.5%
22.6 1
 
0.2%
22.7 2
0.5%
22.8 2
0.5%
22.9 1
 
0.2%
23 3
0.7%
ValueCountFrequency (%)
95.2 1
0.2%
95 1
0.2%
94.8 1
0.2%
94.6 1
0.2%
94.4 1
0.2%
94.2 1
0.2%
94 1
0.2%
93.8 1
0.2%
93.6 1
0.2%
93.4 1
0.2%

Humidity (%)
Real number (ℝ)

HIGH CORRELATION 

Distinct367
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222.94015
Minimum48
Maximum417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:14.869144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile54
Q1132
median214
Q3314
95-th percentile397
Maximum417
Range369
Interquartile range (IQR)182

Descriptive statistics

Standard deviation107.98646
Coefficient of variation (CV)0.48437423
Kurtosis-1.1342963
Mean222.94015
Median Absolute Deviation (MAD)91
Skewness0.062871098
Sum89399
Variance11661.076
MonotonicityNot monotonic
2023-10-22T05:10:15.188894image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 4
 
1.0%
53 4
 
1.0%
50 3
 
0.7%
54 3
 
0.7%
55 3
 
0.7%
49 3
 
0.7%
51 3
 
0.7%
193 3
 
0.7%
207 2
 
0.5%
204 2
 
0.5%
Other values (357) 371
92.5%
ValueCountFrequency (%)
48 1
 
0.2%
49 3
0.7%
50 3
0.7%
51 3
0.7%
52 4
1.0%
53 4
1.0%
54 3
0.7%
55 3
0.7%
56 1
 
0.2%
57 1
 
0.2%
ValueCountFrequency (%)
417 1
0.2%
416 1
0.2%
415 1
0.2%
414 1
0.2%
413 1
0.2%
412 1
0.2%
411 1
0.2%
410 1
0.2%
409 1
0.2%
408 1
0.2%
Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size24.4 KiB
Clear
144 
Rainy
129 
Foggy
128 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters2005
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowRainy
3rd rowClear
4th rowFoggy
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 144
35.9%
Rainy 129
32.2%
Foggy 128
31.9%

Length

2023-10-22T05:10:15.489064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-22T05:10:15.790924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
clear 144
35.9%
rainy 129
32.2%
foggy 128
31.9%

Most occurring characters

ValueCountFrequency (%)
a 273
13.6%
y 257
12.8%
g 256
12.8%
C 144
7.2%
l 144
7.2%
e 144
7.2%
r 144
7.2%
R 129
6.4%
i 129
6.4%
n 129
6.4%
Other values (2) 256
12.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1604
80.0%
Uppercase Letter 401
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 273
17.0%
y 257
16.0%
g 256
16.0%
l 144
9.0%
e 144
9.0%
r 144
9.0%
i 129
8.0%
n 129
8.0%
o 128
8.0%
Uppercase Letter
ValueCountFrequency (%)
C 144
35.9%
R 129
32.2%
F 128
31.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 2005
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 273
13.6%
y 257
12.8%
g 256
12.8%
C 144
7.2%
l 144
7.2%
e 144
7.2%
r 144
7.2%
R 129
6.4%
i 129
6.4%
n 129
6.4%
Other values (2) 256
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 273
13.6%
y 257
12.8%
g 256
12.8%
C 144
7.2%
l 144
7.2%
e 144
7.2%
r 144
7.2%
R 129
6.4%
i 129
6.4%
n 129
6.4%
Other values (2) 256
12.8%

PM2.5
Real number (ℝ)

HIGH CORRELATION 

Distinct375
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346385.93
Minimum13
Maximum1306995.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-10-22T05:10:16.059087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile21
Q1177.1
median80342.3
Q3670749.3
95-th percentile1193982.2
Maximum1306995.2
Range1306982.2
Interquartile range (IQR)670572.2

Descriptive statistics

Standard deviation426989.41
Coefficient of variation (CV)1.2326985
Kurtosis-0.66832887
Mean346385.93
Median Absolute Deviation (MAD)80322.3
Skewness0.90255348
Sum1.3890076 × 108
Variance1.8231996 × 1011
MonotonicityNot monotonic
2023-10-22T05:10:16.409813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 4
 
1.0%
24 4
 
1.0%
17 3
 
0.7%
20 3
 
0.7%
25 3
 
0.7%
16 3
 
0.7%
18 3
 
0.7%
28 3
 
0.7%
26 3
 
0.7%
21 2
 
0.5%
Other values (365) 370
92.3%
ValueCountFrequency (%)
13 1
 
0.2%
14 2
0.5%
15 2
0.5%
16 3
0.7%
17 3
0.7%
18 3
0.7%
19 2
0.5%
20 3
0.7%
21 2
0.5%
22 4
1.0%
ValueCountFrequency (%)
1306995.2 1
0.2%
1301218.2 1
0.2%
1295455.7 1
0.2%
1289707.7 1
0.2%
1283974.2 1
0.2%
1278255.2 1
0.2%
1272550.7 1
0.2%
1266859.7 1
0.2%
1261183.2 1
0.2%
1255521.2 1
0.2%

Interactions

2023-10-22T05:10:04.394026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:36.701841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:42.214971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:46.384376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:50.187191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:56.642161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:00.085056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:02.203967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:04.665202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:37.210130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:42.815492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:46.870928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:50.747186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:57.385065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:00.354155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:02.489988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:04.945687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:37.724699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:43.236408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:47.278467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:51.427620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:57.899622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:00.631615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:02.780146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:05.228631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:38.136052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:43.636560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:47.766347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:52.523516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:58.755690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:00.907829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:03.046651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:05.504164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:38.661417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:44.048389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:48.214293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:53.278991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:59.057748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:01.171587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:03.310634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:05.862787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:39.644685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:44.720269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:48.724507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:54.188280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:59.336500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:01.448173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:03.589406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:06.767703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:40.462289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:45.229374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:49.219534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:55.029062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:59.589841image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:01.707529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:03.854043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:07.137141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:41.342179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:45.811647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:49.687663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:55.859863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:09:59.838693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:01.955492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-22T05:10:04.130667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-22T05:10:16.674653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
IndexPM10 (µg/m³)NO2 (ppb)SO2 (ppb)O3 (ppb)Temperature (°C)Humidity (%)PM2.5LocationCO (ppm)Weather_Condition
Index1.0000.9980.9990.9980.9990.9990.9990.9990.0000.0000.000
PM10 (µg/m³)0.9981.0001.0001.0001.0000.9990.9991.0000.0000.0000.000
NO2 (ppb)0.9991.0001.0001.0001.0000.9990.9991.0000.0000.0000.000
SO2 (ppb)0.9981.0001.0001.0001.0000.9990.9991.0000.0000.0000.000
O3 (ppb)0.9991.0001.0001.0001.0001.0000.9991.0000.0000.0000.000
Temperature (°C)0.9990.9990.9990.9991.0001.0001.0000.9990.0000.0000.000
Humidity (%)0.9990.9990.9990.9990.9991.0001.0000.9990.0000.0000.000
PM2.50.9991.0001.0001.0001.0000.9990.9991.0000.0000.0000.000
Location0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.000
CO (ppm)0.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.000
Weather_Condition0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-10-22T05:10:07.760979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-22T05:10:08.720259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IndexDateLocationPM10 (µg/m³)CO (ppm)NO2 (ppb)SO2 (ppb)O3 (ppb)Temperature (°C)Humidity (%)Weather_ConditionPM2.5
012023-01-01Mumbai200.21053522.550Clear15.0
122023-01-01Pune220.31263822.751Rainy18.0
232023-01-01Nagpur250.41574022.852Clear22.0
342023-01-01Thane280.51884223.053Foggy25.0
452023-02-01Mumbai210.21163623.052Clear16.0
562023-02-01Pune230.31373823.253Clear19.0
672023-02-01Nagpur260.41684123.354Rainy23.0
782023-02-01Thane290.51994323.555Foggy26.0
892023-03-01Mumbai190.21053422.349Clear14.0
9102023-03-01Mumbai180.21053322.048Clear13.0
IndexDateLocationPM10 (µg/m³)CO (ppm)NO2 (ppb)SO2 (ppb)O3 (ppb)Temperature (°C)Humidity (%)Weather_ConditionPM2.5
3913912023-09-04Mumbai369360.2127755454661323480693.4408Clear1255521.2
3923922023-09-04Pune372120.3129978461171358870993.6409Rainy1261183.2
3933932023-09-04Nagpur374890.4132239467841396210293.8410Foggy1266859.7
3943942023-09-04Thane377670.5134540474671435533294.0411Clear1272550.7
3953952023-10-04Mumbai380460.2136881481681476874794.2412Rainy1278255.2
3963962023-10-04Pune383260.3139262488871520269594.4413Foggy1283974.2
3973972023-10-04Nagpur386070.4141684496241565752694.6414Clear1289707.7
3983982023-10-04Thane388890.5144147503801613358994.8415Rainy1295455.7
3993992023-11-04Mumbai391720.2146652511571663123495.0416Foggy1301218.2
4004002023-11-04Pune394560.3149199519551715081395.2417Clear1306995.2